Weather has broad and significant effects on the roadway conditions. Snow, rain, fog, ice, freezing rain, and other weather conditions can impair the ability of drivers to operate their vehicles safely, significantly reduce roadway capacity, and dramatically increase travel times. Roadway conditions, including high-quality weather information about the roadway environment, including both current observations and forecasts, communicated in a timely and effective manner can help drivers to make better decisions regarding travel plans and to react properly when faced with potentially compromised conditions.
Various meteorological modeling systems are known in the art, for example, pavement models and land surface models. Pavement models utilize certain meteorological data (for example, air temperature, dew point, pressure, precipitation data, cloud cover, and wind) and roadway data (for example, road type, surface condition, surface and subsurface temperatures) as inputs. These pavement models provide certain outputs that may include road surface and subsurface temperature, quantity of liquid or frozen precipitation on the road, and road condition.
A land surface model (“LSM”) is a hydrological model used for meteorological applications and provides data related to the fate of precipitation after it reaches the ground. LSMs provide temperature and moisture characteristics of various soil layers. An LSM may use land use/land cover (for example, urban, agricultural, forests, wetlands, bodies of water, and seasonal variability), soil characteristics (for example, clay, sand, silt, and loam), topography, and atmospheric data (for example, high-resolution mesoscale models, observations, and radar) as inputs to determine the amount of precipitation run-off in a particular location. Traditionally, these LSMs are used by meteorologists to predict potential outcomes, for example flooding, resulting from hazardous precipitation, which are then often communicated to the general public by radio or television announcements.
Pavement models and LSMs both have beneficial uses and provide valuable data. However, both models have limitations on the data they provide, and the inputs on which the derived data is based. More specifically, pavement models and LSMs have traditionally been utilized for completely different analysis and to provide completely different outputs. To date, there has been no method or system that incorporates the benefits, and best aspects of a land surface model output data and the pavement model output data to communicate potentially dangerous road conditions to a driver. Neither the LSM nor the pavement model alone allow for accurate communication of dangerous road conditions to a driver. The LSM, while deriving data such as snow pack depth for a particular location, would fail to consider the effect of the location being a paved roadway versus a rural field. The LSM would be further frustrated by the many different pavement types encountered today, including asphalt, concrete and bridges. While an LSM operates on a continuous basis across the continental United States, pavement models known in the art do not. Instead, a pavement model derives a pavement forecast for a single point, which is insufficient as it cannot track the fate of precipitation from surrounding areas after it has fallen to the ground. As is known to those of skill in the art, precipitation may pool or collect in certain locations and such pooled precipitation may present hazards to drivers. This pooled precipitation may also flow into neighboring areas and present an unexpected hazard to drivers.
It is desirable to have a system and method to report the status or condition of roadways to drivers so that they may proceed safely along the roadway. It is also desirable that said roadway status or condition be derived from the most pertinent data from many sources, including a land surface data output. One advantage of the using the land surface data output in generating the roadway status or condition is that the land surface output data provides more accurate prediction of the amount of precipitation on a roadway by taking into account what happens to the precipitation after it has reached the ground, including areas where precipitation is likely to accumulate due to terrain topography and soil conditions. It is also desirable that the system and method report the status or condition of roadways to drivers in an easy to understand manner.